H11M-05:
Utilizing LiDAR Datasets From Experimental Watersheds to Advance Ecohydrological Understanding in Seasonally Snow-Covered Forests
Monday, 15 December 2014: 9:00 AM
Adrian Adam Harpold, University of Nevada Reno, Natural Resources and Environmental Science, Reno, NV, United States, Patrick D Broxton, University of Arizona, Tucson, AZ, United States, Qinghua Guo, University of California Merced, Merced, CA, United States, Michael J Barlage, NCAR/RAL, Boulder, CO, United States and David J Gochis, National Center for Atmospheric Research, Boulder, CO, United States
Abstract:
The Western U.S. is strongly reliant on snowmelt from forested areas for ecosystem services and downstream populations. The ability to manage water resources from snow-covered forests faces major challenges from drought, disturbance, and regional changes in climate. An exciting avenue for improving ecohydrological process understanding is Light Detection and Ranging (LiDAR) because the technology simultaneously observes topography, forest properties, and snow/ice at high-resolution (<10 cm) and over large extents (>100 km2). The availability and quality of LiDAR datasets is increasing rapidly, however they remain under-utilized for process-based ecohydrology investigations. This presentation will illustrate how LiDAR datasets from the Critical Zone Observatory (CZO) network have been applied to advance ecohydrological understanding through direct empirical analysis, as well as model parameterization and verification. Direct analysis of the datasets has proved fruitful for pre- and post-disturbance snow distribution estimates and interpreting in-situ snow depth measurements across sites. In addition, we illustrate the potential value of LiDAR to parameterize and verify of physical models with two examples. First, we use LiDAR to parameterize a land surface model, Noah multi-parameterization (Noah-MP), to investigate the sensitivity of modeled water and energy fluxes to high-resolution forest information. Second, we present a Snow Physics and Laser Mapping (SnowPALM) model that is parameterized with LiDAR information at its native 1-m scale. Both modeling studies demonstrate the value of LiDAR for representing processes with greater fidelity. More importantly, the increased model fidelity led to different estimates of water and energy fluxes at larger, watershed scales. Creating a network of experimental watersheds with LiDAR datasets offers the potential to test theories and models in previously unexplored ways.
